인공지능 (A.I.) 이해와 최신기술 Artificial Intelligence and technical trend.

Lablup Inc.

Mario Cho (조만석)

hephaex@gmail.com

Contents

• Machine Learning?

• Artificial Neural Network?

• Open Source based Artificial Intelligence Softwares

• Open Source A.I Software Applications

Mario (manseok) Cho

Development Experience Image Recognition using Neural Network

Bio-Medical Data Processing

Human Brain Mapping on High Performance Computing

Medical Image Reconstruction(Computer Tomography)

Enterprise System Architect & consuliting

Artificial Intelligence for medicine decision support

Open Source Software Developer Committer: (Cloud NFV/SDN)

Contribute:

TensorFlow (Deep Learning)

OpenStack (Cloud compute)

LLVM (compiler)

Kernel (Linux)

Book Unix V6 Kernel

Lablup Inc.

Mario Cho

hephaex@gmail.com

The Future of Jobs

“The Fourth Industrial Revolution, which

includes developments in previously

disjointed fields such as

artificial intelligence & machine-learning,

robotics, nanotechnology, 3-D printing,

and genetics & biotechnology,

will cause widespread disruption not only

to business models but also to labor

market over the next five years, with

enormous change predicted in the skill

sets needed to thrive in the new

landscape.”

Today‟s information

* http://www.cray.com/Assets/Images/urika/edge/analytics-infographic.html

What is the Machine Learning ?

• Field of Computer Science that evolved from the study of pattern recognition and computational learning theory into Artificial Intelligence.

• Its goal is to give computers the ability to learn without being explicitly programmed.

• For this purpose, Machine Learning uses mathematical / statistical techniques to construct models from a set of observed data rather than have specific set of instructions entered by the user that define the model for that set of data.

Artificial Intelligence

Understand information,

To Learn,

To Reason,

& Act upon it

Object Recognition

What is a neural network?

Yes/No (Mug or not?)

Data (image)

x1λ 5 , x

2λ 5

x2= (W

1´ x

1)+

x3= (W

2´ x

2)+

x1 x2 x3

x4 x5

W4 W3 W2 W1

Neural network vs Learning network Neural Network Deep Learning Network

Neural Network

W1

W2

W3

f(x)

1.4

-2.5

-0.06

Neural Network

2.7

-8.6

0.002

f(x)

1.4

-2.5

-0.06

x = -0.06×2.7 + 2.5×8.6 + 1.4×0.002 = 21.34

Neural Network as a Computational Graph

• In Most Machine Learning Frameworks,

• Neural Network is conceptualized as a Computational Graph

• The simple form of Computational Graph,

• Directed Acyclic Graph consist Data Nodes and Operator Nodes

Y = x1 * x2

Z = x3 – y

Data node

Opeator node

Computational Graph

Tensor

(Muti axis

matrix)

Tensor Tensor

Multifly

add

Function

Single layer perceptron

Affine ReLU X

W b

h1 C

C = ReLU( b + WX )

Multi layer perceptron

X

W1 b1

h1 Affine

a1

W2 b2

h2 Affine

ReLU

ReLU

a2

W3 b3

h3 Affine Softmax

t

Cross

Entropy prob loss

WFO Discovery Advisor

• Researches can‟t innovate fast enough to create truly breakthrough therapies

• To anticipate the safety profile of new treatments

WFO Corpus

Over 1TB of data Over 40m documents

Over 100m entities & relationships

Chemical

12M+ Chemical Structures

Genomics

20,000+ genes

MD Text

50+ books

Medline

23M+ abstracts

Journals

100+ journals

FDA drugs

11,000+ drugs

Patents

16M+ patents

GPU

Tensor Core : NVIDIA Volta

Why is Deep Learning taking off?

Engine

Fuel

Large neural networks

Labeled data

(x,y pairs)

Convolution Feature

Deep learning : CNN

Traditional learning vs Deep Machine Learning

Eiffel Tower

Eiffel Tower

RAW data

RAW data

Deep

Learning

Network

Feature

Extraction

Vectored Classification

Traditional Learning

Deep Learning

Human-Level Object Recognition

• ImageNet

• Large-Scale Visual Recognition Challenge

Image Classification / Localization 1.2M labeled images, 1000 classes Convolutional Neural Networks (CNNs) has been dominating the contest since..

2012 non-CNN: 26.2% (top-5 error)

2012: (Hinton, AlexNet)15.3%

2013: (Clarifai) 11.2%

2014: (Google, GoogLeNet) 6.7%

2015: (Google) 4.9%

Beyond human-level performance

The Big Players

History of Deep Learning Framework

2010

2013

2014

2015

2016

2017

(Nov.)

(Dec.) (Jul.)

(Jun.)

On GitHub

(Debut: Apr. ‘2015)

(Oct.)

(Jun.)

(Nov.)

(Jan.)

(Apr.)

(Mar.)

Google

Open Source Software for Machine Learning

Caffe

Theano

Convnet.js

Torch7

Chainer

DL4J

TensorFlow

Neon

SANOA

Summingbird

Apache SA

Flink ML

Mahout

Spark MLlib

RapidMiner

Weka

Knife

Scikit-learn

Amazon ML

BigML

DataRobot

FICO

Google

prediction API

HPE haven

OnDemand

IBM Watson

PurePredictive

Yottamine

Deep

Learning Stream

Analytics Big Data

Machine Learning

Data

Mining

Machine Learning

As a Service

Pylearn2

• Created by Yangqing Jia (http://daggerfs.com/)

UC Berkerey Computer Science Ph.D. / Trevor Darrell, BAIR

Google BrainLab.TensorFlow join

Facebook research Scientest

Evan Shellhamer (http://imaginarynumber.net/)

• Maintained by BAIR(Berkeley Artificial Intelligence Research, http://bair.berkeley.edu/)

• Release „2013: DeCAF (https://arxiv.org/abs/1310.1531)

Dec. „2013: Caffe v0

• Application Facebook, Adobe, Microsoft, Samsung, Flickr, Tesla, Yelp, Pinterest, etc.

• Motivation „2012 ILSVRC, AlexNet

DNN define/training/deploy implementation by F/W

Caffe http://caffe.berkeleyvision.org/

S/W Creator Platform Mobile Langua

ge Interface OpenMP CUDA OpenCL Multi GPU

Parallel

Executi

on

Caffe BAIR Linux,

Mac - C++

Python,

MATLAB Y

Y

- Y

• Created & Maintained by Preferred Networks, Inc.

(https://www.preferred-networks.jp/ja/)

• Release

Jun. „2015

• Application

Toyota motors, Panasonic (https://www.wsj.com/articles/japan-seeks-tech-revival-with-artificial-intelligence-1448911981)

FANUC (http://www.fanucamerica.com/FanucAmerica-news/Press-releases/PressReleaseDetails.aspx?id=79)

• Motivation

Define-by-Run Architecture

Chainer http://docs.chainer.org/en/latest/index.html

S/W Creator Platform Mobile Langua

ge Interface OpenMP CUDA OpenCL Multi GPU

Parallel

Executi

on

Chainer Preferred

Networks Linux - Python Python -

Y

- Y Y

[Define-and-Run (TensorFlow)] [Define-by-Run (Chainer, PyTorch)]

• Created & Maintained by Microsoft Research

• Release

Jan. „2016

• Applications

Microsoft‟s speech recognition engine

Skype‟s Translator

• Motivation

Efficient performance on distributed environments

CNTK https://www.microsoft.com/en-us/research/product/cognitive-toolkit/

https://www.microsoft.com/en-us/research/blog/microsoft-computational-network-toolkit-offers-most-efficient-distributed-deep-learning-computational-performance/

S/W Creator Platform Mobile Langua

ge Interface OpenMP CUDA OpenCL

Multi

GPU

Parallel

Execution

CNTK Microsoft Linux,

Windows - C++ Python, C++ Y Y - Y Y

• 주체

• Created by Adam Gibson @Skymind (CTO)

Chris Nicholson @Skymind (CEO)

• Maintained by Skymind (https://skymind.ai/)

• Release

Jun. „2014

• Application

Finatial Fraud Detection Research Partnership with Nextremer in Japan (https://skymind.ai/press/nextremer)

DL4J https://deeplearning4j.org/

S/W Creator Platform Mobile Langua

ge Interface OpenMP CUDA OpenCL

Multi

GPU

Parallel

Execution

DL4J SkyMind

Cross-

platform

(JVM)

Android Java Java, Scala,

Python

Y

Y

- Y

Y

(Spark)

• Created & Maintained by

Francois Chollet @Google

• Release

Mar. „2015

• Appliation

TensorFlow (http://www.fast.ai/2017/01/03/keras)

• Motivation

Provide a high-level interface based on deep learning framework like Theano, TensorFlow

Easy to use

Simple Modular

Various Deep-learning framework support

Keras https://keras.io/

S/W Creator Platform Mobile Langua

ge Interface OpenMP CUDA OpenCL

Multi

GPU

Parallel

Execution

Keras François

Chollet

Linux,

Mac,

Windows

- Python Python

Y(Thean

o)

N(TF)

Y

- Y

• Created by CMU (http://www.cs.cmu.edu/~muli/file/mxnet-learning-sys.pdf)

• Maintained by DMLC(Distributed Machine Learning Community)

CMU, NYU, NVIDIA, Baidu, Amazon, etc.

• Release Oct. „2015

• Application AWS (https://www.infoq.com/news/2016/11/amazon-mxnet-deep-learning)

• Motivation Support for Mixed Programming Model: Imperative & Symbolic

Support for Portability: Desktops, Clusters, Mobiles, etc.

Support for Multiple Languages: C++, R, Python, Matlab, Javascript, etc.

MXNet http://mxnet.io/

S/W Creator Platform Mobile Langua

ge Interface OpenMP CUDA OpenCL

Multi

GPU

Parallel

Execution

MXNet DMLC

Linux,

Mac,

Windows,

Javascript

Android,

iOS C++

C++, Python,

Julia,

MATLAB,

JavaScript,

Go, R, Scala,

Perl

Y Y - Y Y

• Created by James Bergstra, Frederic Bastien, etc. (http://www.iro.umontreal.ca/~lisa/pointeurs/theano_scipy2010.pdf_

Maintained by

LISA lab @ Université de Montréal

• Release Nov „2010

• Application Keras

Lasagne

Blocks

• Motivation There‟s any.

Theano http://deeplearning.net/software/theano/index.html

S/W Creator Platform Mobile Langua

ge Interface OpenMP CUDA OpenCL

Multi

GPU

Parallel

Execution

Theano

Université

de

Montréal

Linux,

Mac,

Windows

- Python Python Y

Y

- Y

• Created & Maintained by Ronan Collobert: Research Scientist @ Facebook

Clément Farabet: Senior Software Engineer @ Twitter

Koray Kavukcuoglu: Research Scientist @ Google DeepMind

Soumith Chinatala: Research Engineer @ Facebook

• Release Jul. „2014

• Application Facebook, Google, Twitter, Element Inc., etc.

• Motivation Unlike Caffe, for research rather than mass market

Unlike Theano, easy to use based on imperative model rather than symbolic model

Torch http://torch.ch/

S/W Creator Platform Mobile Langua

ge Interface OpenMP CUDA OpenCL

Multi

GPU

Parallel

Execution

Torch

Ronan,

Clément,

Koray,

Soumith

Linux,

Mac,

Windows

Android,

iOS C, Lua Lua Y

Y

Y Y

Not

officially

• Created & Maintained by Google Brain

• Release Nov. „2015

• Application Google

Search Signals (https://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web-search-over-to-ai-machines)

Email auto-responder (https://research.googleblog.com/2015/11/computer-respond-to-this-email.html)

Photo Search (https://techcrunch.com/2015/11/09/google-open-sources-the-machine-learning-tech-behind-google-photos-search-smart-reply-and-more/#.t38yrr8:fUIZ)

• Motivation It‟s Google

TensorFlow https://www.tensorflow.org/

S/W Creator Platform Mobile Langua

ge Interface OpenMP CUDA OpenCL Multi GPU

Parallel

Executi

on

TensorFlow Google

Linux,

Mac,

Windows

Android,

iOS

C++,

Python

Python,

C/C++, Java,

Go

N Y

- Y Y

Google Tensorflow on github

* Source: Oriol Vinyals – Research Scientist at Google Brain

Expressing High-Level ML Computations

• Core in C++

• Different front ends for specifying/driving the computation

• Python and C++ today, easy to add more

* Source: Jeff Dean– Research Scientist at Google Brain

Hello World on TensorFlow

Image recognition in Google Map

* Source: Oriol Vinyals – Research Scientist at Google Brain

Deep Learning Hello World == MNIST

MNIST (predict number of image)

CNN (convolution neural network) training

MNIST code

Old Character Recognition

Convolution Neural Network

Convolution Neural Network

Multy layer Deep Networks

Face extraction method

Human-Level Face Recognition

• Convolutional neural networks based face recognition system is dominant

• 99.15% face verification accuracy on

LFW dataset in DeepID2 (2014) Beyond human-level recognition

Source: Taigman et al. DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR’14

Deep & Deep Neural network

ImangeNet Classification Top-5 error

ImageNet Large Scale Visual Recognition Challenge

Image Recognition

* Source: Oriol Vinyals – Research Scientist at Google Brain

Object Classification and Detection

Language Generating

* Source: Oriol Vinyals – Research Scientist at Google Brain

How to the Object recognition ?

Image Caption Generation

Black/White Image Colorization

Colorful Image Colorization

ab L

Concatenate (L,ab) Grayscale image: L channel

“Free”

supervisory

signal

Semantics?

Higher-level

abstraction?

Ref: Richard Zhang, Phillip Isola, Alexei (Alyosha) Efros

Inceptions

Neuro Style Painter

Neuro Style Painter

Neuro Style Painter

Neuro paint

pix2pix

Edges2Image

[Isola et al. CVPR 2017]

Image Generate

3D Generative Adversarial Network

[Wu et al. NIPS 2016]

Image Segmentation

Scene Parsing

[Farabet et al. ICML 2012, PAMI 2013]

Scene Parsing

[Farabet et al. ICML 2012, PAMI 2013]

Auto pilot car

Neural Conversational Model

Recurrent Neural Language modeling

RNN Unfold into DNN over time

Goolge Natural Translate Machine

Natural Language Translate

Deep Q-Learning

AlphaGo Gen1

How do data science techniques scale with amount of data?

Inspirer Humanity

Thanks you!

Q&A